Yep, I think you're right, for this sample there are many points away from the y=x line.
<https://lh4.googleusercontent.com/-rLT39dQUYe0/Uq9aLgEKsPI/AAAAAAAAKWY/Uv1cJZGoVTo/s1600/2013-12-16_KB170TvsKB170B_beta_TvsN.png> Thankfully this isn't the case for all the samples though. I suppose other than finding the right match algorithmically (as this is public data), I could use a pooled reference instead of the matched normal. Two more questions that I haven't been able to find in the documentation.. 1- Is there a way to remove (or ideally prevent) very small segments? I'm finding several small (2-10 probes) segments with very high copy number (>10). I'm doubtful that they are all real. 2- I am finding very few deletions (<5 per genome, vs dozens of amplifications) in my samples. I'm calling gains/dels based on the standard deviation of the median probe log-ratio. Is this skewed distribution a common phenomena and should my threshold for deletions be more lenient than my threshold for gains? Thanks! Emilie On Friday, December 13, 2013 4:55:26 PM UTC-5, Henrik Bengtsson wrote: > > On Fri, Dec 13, 2013 at 11:15 AM, Emilie <emilie....@gmail.com<javascript:> > > wrote: > >> Thanks again Henrick. I do see 3 bands, but not sure they are necessarily >> clean/distinct. >> >> >> <https://lh5.googleusercontent.com/-yPngBXg2loA/Uqtcqi2z4YI/AAAAAAAAKWI/mcUOS0DWiyQ/s1600/2013-12-13_KB170B_BAF.png> >> > > These normal BAFs look ok to me. > > >> <https://lh5.googleusercontent.com/-yPngBXg2loA/Uqtcqi2z4YI/AAAAAAAAKWI/mcUOS0DWiyQ/s1600/2013-12-13_KB170B_BAF.png> >> A similar but slightly noisier pattern is observed in the tumour sample. >> I also down-sampled the data 50x to be able to see the patterns (vs a black >> blob). Would you consider this as noise/a bad run? >> > > Down-sampling is alright when plotting whole-genome data. > > If the tumor BAFs ('betaT') are not much noisier than the normal BAFs > ('betaN'), I suspect a mismatched pair. Next, look at tumor vs normal > BAFs, e.g. > > plot(betaN, betaT, xlim=c(0,1), ylim=c(0,1)) > > What do you get? You should see most data points scattered along the > diagonal line, similar to the ones in Figure 4 of the online CalMaTe > vignette [ http://aroma-project.org/vignettes/CalMaTe ]. If you see data > points all over the place, particularly in upper-left and the lower-right > corners, your tumor normal pair is not from the same patient. > > /Henrik > > >> Emilie >> >> >> >> On Thursday, December 12, 2013 6:01:48 PM UTC-5, Henrik Bengtsson wrote: >> >>> The tumor DH panel makes me believe that either your tumor or your >>> normal chip data is bad, or alternatively that the tumor and normal are >>> not matched. >>> >>> Check the allele B fraction of your normal. It should show three >>> distinct bands. Do the same for the tumor. It should also show distinct >>> bands with varying of bands depending on aberrations. If both look clean, >>> then it's likely they're not matched. If one is very noisy, then that one >>> is simply a bad run/sample. >>> >>> Henrik >>> On Dec 12, 2013 12:42 PM, "Emilie" <emilie....@gmail.com> wrote: >>> >>>> Thank you both very much! I was indeed referring to smooth.cna, sorry >>>> about that confusion. >>>> >>>> I've switched over to PSCBS and used the dropSegmentationOutliers- it >>>> seems to be running well. I've noticed that some of my samples have very >>>> fragmented profiles (see attached). Does this suggest poor quality data, >>>> or >>>> maybe an error in my normalization/plotting? Not all samples are like >>>> this, >>>> but it almost seems like the order of the of the probes is scrambled? >>>> >>>> >>>> Emilie >>>> >>>> >>>> On Thursday, December 5, 2013 1:08:46 PM UTC-5, Henrik Bengtsson wrote: >>>>> >>>>> Pierre beat me to this one. Comments below... >>>>> >>>>> On Thu, Dec 5, 2013 at 9:20 AM, Pierre Neuvial >>>>> <pierre....@genopole.cnrs.fr> wrote: >>>>> > Hi Emilie, >>>>> > >>>>> > OK, so you are referring to the “smooth.CNA" function in the >>>>> DNAcopy >>>>> > package, cf >>>>> > http://www.bioconductor.org/packages/2.13/bioc/vignettes/DNA >>>>> copy/inst/doc/DNAcopy.pdf >>>>> > >>>>> > What this function is doing is detecting outliers (based on how far >>>>> their >>>>> > signal value is from their neighbors) and shrink their signal values >>>>> toward >>>>> > those of their neighbors. >>>>> > >>>>> > This is indeed appropriate and recommended. I thought that by >>>>> "smoothing" >>>>> > you meant performing some kind of local averaging of the original >>>>> signal >>>>> > (e.g. using a mobile median or by binning): this I don't recommend. >>>>> Sorry >>>>> > for the confusion. >>>>> > >>>>> > >>>>> > To drop outliers, one possibility is to use the >>>>> "dropSegmentationOutliers" >>>>> > function from the PSCBS package. See the vignettes at >>>>> > http://cran.fhcrc.org/web/packages/PSCBS/index.html >>>>> > >>>>> > Another comment: since you are following the vignette for paired CNA >>>>> > analysis, I am guessing that you are working with tumor/normal >>>>> pairs. If >>>>> > so, then you should use PSCBS rather than CBS for segmentation. >>>>> PSCBS is an >>>>> > extension of CBS to segment not only total copy numbers but also >>>>> allelic >>>>> > ratios. See the PSCBS vignette in the above URL. >>>>> >>>>> To balance this a little bit, I would say there may exist outliers in >>>>> the total copy number (TCN) signals that are so sever that they bias >>>>> the estimators/test statistic of CBS (which assumes Gaussian signals). >>>>> If one believes there are such outliers and worries that they are so >>>>> extreme that they would affect the segmentation severely, one could >>>>> either (i) drop or (ii) shrink ("smooth") them. In the vignettes of >>>>> the PSCBS package, I've last night [PSCBS (>= 0.39.8)] >>>>> corrected/clarified Section 'Dropping TCN outliers' to say the >>>>> following: >>>>> >>>>> "There may be some outliers among the TCNs. In >>>>> CBS~\citep{OlshenA_etal_2004,VenkatramanOlshen_2007}, the authors >>>>> propose a method for identifying outliers and then to shrink such >>>>> values toward their neighbors ("smooth") before performing >>>>> segmentation. At the time CBS was developed it made sense to not just >>>>> to drop outliers because the resolution was low and every datapoint >>>>> was valuable. With modern technologies the resolution is much higher >>>>> and we can afford dropping such outliers, which can be done by: >>>>> >>>>> > data <- dropSegmentationOutliers(data) >>>>> >>>>> Dropping TCN outliers is optional." >>>>> >>>>> Hope this clarifies. >>>>> >>>>> Back to the original question: It is not possible to drop (or smooth) >>>>> outliers using the CbsModel() pipeline [I'll add that to the todo >>>>> list]. The easiest is to turn use the PSCBS package, where you can do >>>>> plain old single-track CBS segmentation, paired PSCBS segmentation and >>>>> also non-paired PSCBS segmentation. As Pierre says, if you have tumor >>>>> SNP data, you should look into doing parent-specific CN analysis, >>>>> which you can do either via paired or non-paired PSCBS depending on >>>>> whether you have match normals or not. >>>>> >>>>> To take your allele-specific CRMAv2 and bring it into a format >>>>> recognized by the PSCBS package, see >>>>> http://aroma-project.org/vignettes/PairedPSCBS-lowlevel >>>>> >>>>> /Henrik >>>>> >>>>> > >>>>> > Best, >>>>> > >>>>> > Pierre >>>>> > >>>>> > >>>>> > On Wed, Dec 4, 2013 at 5:29 PM, Emilie <emilie....@gmail.com> >>>>> wrote: >>>>> >> >>>>> >> Hi Pierre, >>>>> >> >>>>> >> Thanks for your answer. I may be wrong but I thought smoothing >>>>> prior to >>>>> >> segmentation was somewhat common. It is shown in the vignettes for >>>>> DNACopy >>>>> >> and seems to be fairly common in the literature (this approach was >>>>> used in >>>>> >> the Metabric paper for example, >>>>> >> http://www.ncbi.nlm.nih.gov/pubmed/22522925). >>>>> >> >>>>> >> I'd be interested in hearing more of your thoughts against this. Do >>>>> you >>>>> >> have an idea of how much resolution is lost by smoothing? >>>>> >> >>>>> >> Emilie >>>>> >> >>>>> >> >>>>> >> >>>>> >> On Tuesday, December 3, 2013 5:26:38 PM UTC-5, Pierre Neuvial >>>>> wrote: >>>>> >>> >>>>> >>> Hi Emilie, >>>>> >>> >>>>> >>> It's certainly possible to do this within the Aroma framework >>>>> (e.g. using >>>>> >>> the function "binnedSmoothing"). It's probably not as >>>>> straightforward as >>>>> >>> running the segmentation directly, though, because this is not a >>>>> typical use >>>>> >>> case. >>>>> >>> >>>>> >>> In fact, I'm not sure why you want to perform smoothing before >>>>> >>> segmentation ? Smoothing is definitely not required before >>>>> segmentation, >>>>> >>> and I would actually discourage to go this path because it will >>>>> end up in a >>>>> >>> loss of resolution along the genome at the smoothing step. >>>>> >>> >>>>> >>> Best, >>>>> >>> >>>>> >>> Pierre >>>>> >>> >>>>> >>> >>>>> >>> On Tue, Dec 3, 2013 at 8:53 PM, Emilie <emilie....@gmail.com> >>>>> wrote: >>>>> >>>> >>>>> >>>> Hi there, >>>>> >>>> >>>>> >>>> I'm new to processing Affy SNP6 chips and so am mainly >>>>> experimenting >>>>> >>>> with different methods to date. I ran CRMAv2 and followed steps >>>>> 1-4 from the >>>>> >>>> vignette (http://aroma-project.org/vignettes/CRMAv2). For step >>>>> 5, I want to >>>>> >>>> do a paired analysis. >>>>> >>>> >>>>> >>>> Previously I've used DNAcopy to perform CBS for other array >>>>> types, and >>>>> >>>> would like to follow a similar procedure, which includes >>>>> smoothing prior to >>>>> >>>> segmentation. Is this possible using the aroma.affymetrix >>>>> package? So far >>>>> >>>> I've followed the vignette for paired CNA analysis >>>>> >>>> (http://aroma-project.org/vignettes/pairedTotalCopyNumberAnalysis) >>>>> but >>>>> >>>> haven't seen any options for smoothing. >>>>> >>>> >>>>> >>>> thank you very much, >>>>> >>>> >>>>> >>>> emilie >>>>> >>>> >>>>> >>>> -- >>>>> >>>> -- >>>>> >>>> When reporting problems on aroma.affymetrix, make sure 1) to run >>>>> the >>>>> >>>> latest version of the package, 2) to report the output of >>>>> sessionInfo() and >>>>> >>>> traceback(), and 3) to post a complete code example. >>>>> >>>> >>>>> >>>> >>>>> >>>> You received this message because you are subscribed to the >>>>> Google >>>>> >>>> Groups "aroma.affymetrix" group with website >>>>> http://www.aroma-project.org/. >>>>> >>>> To post to this group, send email to aroma-af...@googlegroups.com >>>>> >>>> >>>>> >>>> To unsubscribe and other options, go to >>>>> >>>> http://www.aroma-project.org/forum/ >>>>> >>>> >>>>> >>>> --- >>>>> >>>> You received 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>> --- >>>>> >> You received this message because you are subscribed to the Google >>>>> Groups >>>>> >> "aroma.affymetrix" group. >>>>> >> To unsubscribe from this group and stop receiving emails from it, >>>>> send an >>>>> >> email to aroma-affymetr...@googlegroups.com. >>>>> >> For more options, visit https://groups.google.com/groups/opt_out. >>>>> > >>>>> > >>>>> > -- >>>>> > -- >>>>> > When reporting problems on aroma.affymetrix, make sure 1) to run the >>>>> latest >>>>> > version of the package, 2) to report the output of sessionInfo() and >>>>> > traceback(), and 3) to post a complete code example. >>>>> > >>>>> > >>>>> > You received this message because you are subscribed to the Google >>>>> Groups >>>>> > "aroma.affymetrix" group with website http://www.aroma-project.org/. >>>>> >>>>> > To post to this group, send email to aroma-af...@googlegroups.com >>>>> > To unsubscribe and other options, go to >>>>> http://www.aroma-project.org/forum/ >>>>> > >>>>> > --- >>>>> > You 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